import health
import matplotlib.pyplot as plt
from textblob import TextBlob
from wordcloud import WordCloud
diseaseSearch = 'disease'
list_of_report = health.get_all_reports()
combinedDiseases = ''
for dis in list_of_report:
combinedDiseases += ' ' + dis['disease']
healthBlob = TextBlob(combinedDiseases)
diseaseDictionary = dict()
for word in healthBlob.words:
diseaseDictionary[word.lower()] = healthBlob.word_counts[word.lower()]
wordCloud = WordCloud(
background_color='white').generate_from_frequencies(diseaseDictionary)
plt.title('Diseases in the US')
plt.axis('off')
for dis in list_of_report:
healthBlob = TextBlob(dis['disease'])
plt.imshow(wordCloud, interpolation='bilinear')
from course import corgis
import health
data = health.get_all_reports()
data = corgis.data2frame(data)
data.to_csv('health.csv',index=False)
return state_index_values
def state_list(reports, year):
states = []
for report in reports: # makes a list of all states
if report["year"] == year:
if report["loc"] not in states:
states.append(report["loc"])
#print(report["loc"], " pop =", report["population"])
return states
#------------------------------------------------------------------------------
reports = health.get_all_reports()
states = state_list(reports, 2000)
population2000 = annual_pop(reports, 2000)
most_populated2000 = most_populated(population2000)
pop_state_index2000 = find_pop_index_values(most_populated2000, population2000)
most_populated_states2000 = find_states(pop_state_index2000, states)
number_sick2000 = number_sick(reports, 2000)
most_sick2000 = most_sick(number_sick2000)
sick_state_index2000 = find_sick_index_values(most_sick2000, number_sick2000)
most_sick_states2000 = find_states(sick_state_index2000, states)